Transformer and GAN-Based Super-Resolution Reconstruction Network for Medical Images

Super-resolution reconstruction in medical imaging has become more demanding due to the necessity of obtaining high-quality images with minimal radiation dose, such as in low-field magnetic resonance imaging (MRI). However, image super-resolution reconstruction remains a difficult task because of the complexity and high textual requirements for diagnosis purpose. In this paper, we offer a deep learning based strategy for reconstructing medical images from low resolutions utilizing Transformer and generative adversarial networks (T-GANs). The integrated system can extract more precise texture information and focus more on important locations through global image matching after successfully inserting Transformer into the generative adversarial network for picture reconstruction. Furthermore, we weighted the combination of content loss, adversarial loss, and adversarial feature loss as the final multi-task loss function during the training of our proposed model T-GAN. In comparison to established measures like peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM), our suggested T-GAN achieves optimal performance and recovers more texture features in super-resolution reconstruction of MRI scanned images of the knees and belly.